0. Annotate cell types for all the cells

mkdir -p example/cfa

[ -f example/cfa/psych.annot.gz ] || \
    ../src/mmutil_annotate_col \
        --mtx data/brain_2018-05-03/filtered_count_matrix.mtx.gz \
        --row data/brain_2018-05-03/features.tsv.gz \
        --col data/brain_2018-05-03/barcodes.tsv.gz \
        --ann PsychENCODE.marker \
        --batch_size 1000 \
        --out example/cfa/psych

1. Extract cells with underlying phenotypes are annotated

meta.dt <- fread("data/brain_2018-05-03/filtered_column_metadata.txt.gz")
pheno.dt <- fread("data/brain_2018-05-03/phenotypes.csv")
model.file <- "example/cfa/pheno_model.rdata"

##########################################
## Propensity for pathological AD model ##
##########################################

if(file.exists(model.file)) {
    load(model.file)
} else {
    dir.create("example/cfa/", recursive = TRUE, showWarnings = FALSE)
    annot.dt <- .fread("example/cfa/psych.annot.gz")
    colnames(annot.dt) <- c("TAG", "celltype", "prob", "ln.prob")

    train.data <- pheno.dt[abs(pathoAD) <= 1 &
                           np_sqrt != -9 &
                           nft_sqrt != -9]

    prop.model <- glm(pathoAD ~ np_sqrt + nft_sqrt,
                      data = train.data,
                      family="binomial")

    save(list=c("prop.model"), file=model.file)
}

################################################
## these are cells with annotated individuals ##
################################################

.tag.file <- "example/cfa/input.tags.gz"
if(!file.exists(.tag.file)) {
    meta.pheno.dt <- merge(meta.dt, pheno.dt)[np_sqrt != -9 & nft_sqrt != -9][]
    .fwrite(meta.pheno.dt[, .(TAG)], .tag.file)
}

2. Create another file set for COCOA analysis

# Expected: input.{mtx,cols}.gz
# Column order may change
[ -f example/cfa/input.mtx.gz ] || \
    ../src/mmutil_select_col \
        data/brain_2018-05-03/filtered_count_matrix.mtx.gz \
        data/brain_2018-05-03/filtered_count_matrix.cols.gz \
        example/cfa/input.tags.gz \
        example/cfa/input
############################################################
## prepare for the input files matching the same order of ##
## cells/columns                                          ##
############################################################

.hdr <- "example/cfa/input"
.trt.file <- .hdr %&% ".trt.gz"
.ind.file <- .hdr %&% ".ind.gz"
.annot.file <- .hdr %&% ".annot.gz"
.lab.file <- .hdr %&% ".lab.gz"

if(any(!file.exists(.trt.file, .annot.file, .ind.file, .lab.file))) {
    cols.dt <- .fread("example/cfa/input.cols.gz") %>% 
        (function(x){ colnames(x)=c("TAG"); x }) %>% 
        left_join(meta.pheno.dt) %>%
        left_join(annot.dt)

    xx = cols.dt[, .(np_sqrt, nft_sqrt)]

    y.pheno <- data.table(y = predict(prop.model, xx)) %>%
        mutate(y = if_else(y > 0, "AD", "HC"))

    ind.dt <- cols.dt[, .(projid)] %>%
        mutate(y = unlist(y.pheno)) %>%
        mutate(ind = projid %&% "_" %&% y)

    .fwrite(ind.dt[, .(ind)], "example/cfa/input.ind.gz")
    .fwrite(y.pheno, "example/cfa/input.trt.gz")
    .fwrite(cols.dt[, .(TAG, celltype)], "example/cfa/input.annot.gz")
    .fwrite(cols.dt[, .(celltype)] %>% unique, "example/cfa/input.lab.gz")
}

3. Run the counterfactual adjustment model

# Just to have this in the same location
[ -f example/cfa/input.rows.gz ] || \
    cp data/brain_2018-05-03/filtered_gene_row_names.txt.gz \
       example/cfa/input.rows.gz

# Basic aggregate statistics
[ -f example/cfa/output.sum.gz ] || \
    ../src/mmutil_aggregate_col \
        --mtx example/cfa/input.mtx.gz \
        --col example/cfa/input.cols.gz \
        --annot example/cfa/input.annot.gz \
        --lab example/cfa/input.lab.gz \
        --ind example/cfa/input.ind.gz \
        --out example/cfa/output

# CF-adjusted aggregate statistics
[ -f example/cfa/output.resid_mu.gz ] || \
    ../src/mmutil_cfa_col \
        --mtx example/cfa/input.mtx.gz \
        --col example/cfa/input.cols.gz \
        --annot example/cfa/input.annot.gz \
        --trt example/cfa/input.trt.gz \
        --lab example/cfa/input.lab.gz \
        --ind example/cfa/input.ind.gz \
        --knn 10 --rank 30 --bilink 100 --log_scale --normalize \
        --out example/cfa/output
MIN.TOT.CUTOFF <- 50
.data.file <- "example/cfa/stat_tab.rdata"
if(!file.exists(.data.file)) {
    stat.tab <- .read.stat.all("example/cfa/output", tot.cutoff = MIN.TOT.CUTOFF)
    save(stat.tab, file = .data.file)
} else {
    load(.data.file)
}

stat.avg.tab <- .take.average(stat.tab, "AD", tot.cutoff = MIN.TOT.CUTOFF)

First, show confounders and genes associated with the confounding effects

.mat <- stat.tab[tot > 0, .(mu=sum(cf.mu)), by=.(gene, col)] %>%
    spread(key=col, value=mu, fill=0)

.ln.mat <- log(1 + .mat[, -"gene"]) %>% as.matrix %>% t %>% scale
.ln.mat[is.na(.ln.mat)] <- 0

gene.cf.assoc.dt <- test.gene.association(.ln.mat) %>%
    na.omit %>%
    rename(cf.beta = beta, cf.se = se, cf.pv = p.val) %>%
    as.data.table
top.genes <- 
    gene.cf.assoc.dt[order(gene.cf.assoc.dt$cf.pv),
                     head(.SD, 3),
                     by = .(pheno, celltype)] %>%
    filter(cf.pv < 5e-4) %>% 
    select(gene) %>% unique %>% unlist

top.cf.assoc.dt <- gene.cf.assoc.dt[gene %in% top.genes] %>% 
    na.omit %>% 
    mutate(row = gene) %>%
    mutate(col = pheno %&% "@" %&% celltype) %>%
    mutate(weight = -log10(pmax(cf.pv, 1e-8))) %>% 
    order.pair(ret.tab = TRUE) %>%
    as.data.table
## Loading required package: cba
## Loading required package: proxy
## 
## Attaching package: 'proxy'
## The following objects are masked from 'package:stats':
## 
##     as.dist, dist
## The following object is masked from 'package:base':
## 
##     as.matrix
plt <-
    .gg.plot(top.cf.assoc.dt, aes(y=row, x=col, fill=celltype, size=weight)) +
    theme(title = element_text(size=8)) +
    theme(axis.text.x = element_blank()) +
    theme(axis.ticks.x = element_blank()) +
    theme(axis.title = element_blank()) +
    ggtitle("Top " %&% length(top.genes) %&% " genes ~ confounding effects") +
    facet_grid(.~pheno, scales="free", space="free") +
    geom_point(pch=22, stroke=.1) +
    scale_fill_brewer(palette = "Paired") +
    scale_size_continuous("p-value",
                          range=c(0,3),
                          labels = function(x) num.sci(10^(-x)))

.file <- fig.dir %&% "/Fig_Confounding_Genes.pdf"
.gg.save(filename = .file, plot = plt, width=5, height=5)
print(plt)

PDF

.row.df <- match.row.with.pheno(.ln.mat)

.tsne <- Rtsne::Rtsne(.ln.mat, n_threads=16)
colnames(.tsne$Y) <- "tSNE" %&% 1:ncol(.tsne$Y)

.tsne.dt <- cbind(.row.df, .tsne$Y) %>% as.data.table

p1 <- .gg.plot(.tsne.dt, aes(x=tSNE1, y=tSNE2)) +
    theme(legend.position=c(1,1), legend.justification = c(1,1)) +
    geom_point(aes(fill=celltype), alpha=.8, stroke=.2, size=1.25, pch=21) +
    scale_fill_brewer("", palette = "Paired")

p2 <- .gg.plot(.tsne.dt %>% arrange(desc(disease)), aes(x=tSNE1, y=tSNE2)) +
    theme(legend.position=c(1,1), legend.justification = c(1,1)) +
    geom_point(aes(fill=disease), alpha=.5, stroke=.2, size=1.25, pch=21) +
    scale_fill_manual("", values=c("#FF9999","#FFFFFF"))

plt <- p1 | p2

.file <- fig.dir %&% "/Fig_Confounding_tSNE.pdf"
.gg.save(filename = .file, plot = plt, width=6, height=4)
print(plt)

PDF

Global statistics

.file <- fig.dir %&% "/Fig_histogram_pvalue.pdf"
pdf(.file, width = 4, height = 4)
hist(stat.avg.tab$pv.z, main="", xlab="gene-level p-value")
dev.off()
## quartz_off_screen 
##                 2
hist(stat.avg.tab$pv.z, main="", xlab="gene-level p-value")

PDF

Marginal statistics without confounding factor adjustment

gene.obs.assoc.dt <- 
    stat.tab[, .(mu=sum(tot)), by=.(gene, col)] %>%
    spread(key=col, value=mu, fill=0) %>% 
    (function(x) log(1 + x[, -"gene"])) %>%
    (function(x) scale(t(as.matrix(x)))) %>%
    (function(x) { x[is.na(x)] <- 0; x }) %>%
    test.gene.association %>%
    na.omit %>%
    rename(obs.beta = beta, obs.se = se, obs.pv = p.val) %>%
    as.data.table
gene.assoc.dt <- stat.avg.tab %>% na.omit %>%
    merge(gene.ontology$coding, by.x = "gene", by.y = "hgnc_symbol")

gene.assoc.dt <- gene.assoc.dt[order(gene.assoc.dt$pv.z),
                               head(.SD, 1),
                               by=.(gene, celltype)]

gene.assoc.dt <- gene.assoc.dt %>% 
    left_join(gene.obs.assoc.dt[pheno=="pathoAD"], by = c("gene", "celltype")) %>% 
    left_join(gene.cf.assoc.dt[pheno=="pathoAD"], by = c("gene", "celltype", "pheno"))

PV.CUTOFF <- 0.05/nrow(gene.assoc.dt)
plot.genome.view <- function(ct, .tot.dt, .cutoff){

    .chr.names <- c(as.character(1:22), "X", "Y", "MT")

    .dt <- .tot.dt[celltype == ct & chromosome_name %in% .chr.names] %>%
        mutate(chr = factor(chromosome_name, .chr.names)) %>% 
        mutate(z.trunc = pmin(pmax(z, -4), 4))

    .dt.show <- .dt[pv.z < .cutoff & abs(ln.mu) > .1]

    .dt.show.lab <- .dt.show[order(.dt$pv.z), head(.SD, 3), by=.(chr)] %>%
        filter(nchar(gene) > 0) %>% 
        na.omit

    x.lab.fn <- function(x) num.int(x/1e6)
    y.lab.fn <- function(x) num.sci(10^(-x))

    .aes <- aes(x=transcript_start,
                y=-log10(pmax(pv.z, 1e-30)),
                fill=z.trunc)

    ret <-
        .gg.plot(.dt, .aes) +
        ggtitle(ct) +
        theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 5)) +
        theme(title = element_text(size=8)) +
        theme(strip.text = element_text(size=6)) +
        facet_grid(. ~ chr, scales="free", space="free") +
        geom_hline(yintercept=-log10(.cutoff), size=.1, col="red") +
        scale_x_continuous(labels=x.lab.fn) +
        scale_y_continuous(labels=y.lab.fn) +
        ylab("p-value") + xlab("genomic locations (mb)") +
        scale_fill_gradient2(low="blue", high="red", guide=FALSE) +
        geom_point(pch=19, col="gray60", size=.5)

    if(nrow(.dt.show) > 0){
        ret <- ret + geom_point(data = .dt.show, pch=21)
    }

    if(nrow(.dt.show.lab) > 0){
        ret <- ret +
            ggrepel::geom_text_repel(data = .dt.show.lab, aes(label = gene), size = 1)
    }
    return(ret)
}
.ct.valid <- gene.assoc.dt[pv.z < PV.CUTOFF & abs(ln.mu) > .1] %>%
    na.omit %>%
    select(celltype) %>%
    unique %>%
    unlist

p.list <- 
    setdiff(.ct.valid, c("Per","Endo")) %>% 
    lapply(plot.genome.view, .tot.dt = gene.assoc.dt, .cutoff = PV.CUTOFF)

plt <- wrap_plots(p.list, ncol = 1)

.file <- fig.dir %&% "/Fig_Genome_View.pdf"
.gg.save(filename = .file, plot = plt, width=8, height=8)
print(plt)

PDF

Local statistics

MTRNR2L1

DHFR

HSP90AA1

MTRNR2L8

SPP1

B2M

ARL17B

ELK1

EIF5A

NIPAL2

PLXDC2

MTRNR2L12

SPDYE2

GFAP

SLC1A3

MT2A

SNCG

TMSB10

NTM

CLDN5

HSPA8

CHI3L1

NNAT

P2RY14

DSCAM

LRPAP1

HSPA1B

RASGEF1B

SSTR2

STAT4

CNTN6

IFITM3

APOE

RPH3A

ITPR2

PDE5A

HLA-B

SLC26A3

SEMA3A

OR2L13

PRKG1

MBNL1

PTPN14

LRRC4C

BSG

DOCK8

TMSB4X

EEF1A2

HS3ST4

FKBP5

DPP10

FRMD4A

TSC22D1

RPS19

CACNA1A

NEBL

LRRC37A3

CPE

ARHGAP24

PTCHD4

FLT1

IQCJ-SCHIP1

LRP1B

APOD

FTL

NOVA1

DOCK10

PCSK1N

PCED1B

TLE4

NLGN1

MEF2C

HDAC9

EGR1

VGF

PTPRZ1

AQP1

VSTM2L

ADCY2

UBE2E2

TRPM3

FOXN3

FMN1

ELMO1

CD81

PRKX

RPL13

NBPF11

LINGO1

LHFPL2

TEF

CSGALNACT1

DOCK4

GRID2

CLU

CHRM5

LRRC37A2

TSC22D3

VCAN

ADAMTS2

MAML2

SNED1

IGFBP7

RBMS3

CNGB1

PICALM

BHLHE41

MSH4

HLA-C

PLA2G4A

VSIG4

FGD4

TBC1D5

SLC38A2

FOS

ST18

OTOGL

CSF1R

NRG3

RGS6

ABCA6

DLC1

GJA1

DST

PTPRM

MEF2A

ACTB

LAPTM5

CELF2

SNCA

ADGRL3

PTMA

RASD2

DUSP1

ZNF365

SLC9A9

HSPB1

HLA-A

DNAJB1

EEF1A1

GLDN

RUNX1

GNAQ

KCNIP4

HRH3

OXR1

RPS25

ITGB4

FGF12

RPS15

GPS2

CYFIP1

DUSP26

FYN

OLIG1

ANKRD44

DUSP6

PSAP

EIF1

PMPCB

SSH2

NHSL1

ADAMTS18

NPTX2

RPLP1

SH3RF3

APOC1

RPL13A

TBXAS1

LDLRAD4

KCNV2

EPAS1

RPL10

ST6GALNAC3

CD44

IFI27

HLA-DRB1

APBB1IP

ABCB1

ZFHX3

ADGRG1

HSD11B1

SKAP2

DPYD

MSMO1

SLTM

KAZN

LURAP1L

PHLDB2

PCSK6

PTPRG

MAN2A1

JAZF1

PILRB

ALCAM

CENPP

RPRM

MYOM2

YIPF6

LAMP5

TSPAN17

RPL28

IL34

SYT6

RHOBTB1

TMEM233

ST6GAL1

EPB41L2

HSPA1A

CPQ

STARD13

RFX4

IL1RAP

CTXN3

ZFP36L1

TIMP2

ZDHHC2

LAMA2

CD24

PTGDS

TANC2

C1QC

ZEB2

TAB2

DUSP8

CRYAB

IGFBP5

PDK4

SPARCL1

MAPK4

SMAD4

PLEKHA5

PNISR

GPM6B

FAM171A1

AUTS2

EIF4G3

NR4A1

SRGAP2

RPL41

CD53

TMED2

SYNE1

TCF7L2

ATRNL1

PLEC

CNTN1

CARNS1

HUNK

AEBP1

TMEM144

TPT1

LIFR

PPAT

HLA-E

MT1E

IFNGR1

CFAP61

LPCAT2

FHIT

ITPKB

GRIK1

P2RY12

TTF2

RPS27A

GRIK3

N4BP2L2

FOXP1

SPIDR

FLRT2

SPTSSB

TNC

SHTN1

MTRNR2L6

PPP2R2B

CD74

BNC2

TCF12

SYTL4

RAP1A

DTNA

MED13L

MDGA2

COBLL1

SLC8A1

PLD1

RHOBTB3

HTR4

MYO1E

MT3

NKX6-2

MKNK2

SORL1

PARD3

ARHGAP15

PTPN13

ANKRD12

NCAM1

BAZ2B

ZBTB20

MAML3

ATM

DCC

SORCS2

ZDHHC22

SON

SAMD12

PDE4DIP

SIK3

ZFP36L2

CBFB

WWOX

TNRC6B

COMMD2

DIAPH2

C3

NEDD9

LPIN1

CREG1

TTC3

ARMC9

TENM4

GPATCH2L

Significant genes

.cell.count <- .fread("example/cfa/input.annot.gz") %>%
    (function(x) { colnames(x) <- c("TAG", "celltype"); x }) %>%
    (function(x) x[, .(Cell = .N), by=.(celltype)]) %>% 
    (function(x) x[, p.Cell := 100*Cell/sum(Cell)])

.gene.count <- 
    .gene.table[, .(Gene = .N), by = .(celltype)] %>% 
    (function(x) x[, p.Gene := 100*Gene/sum(Gene)])

.aes.cell <- aes(y=celltype, yend=celltype, x=0, xend=-Cell, colour = celltype, label=num.int(Cell) %&% " (" %&% num.round(p.Cell) %&% "%)")

.aes.gene <- aes(y=celltype, yend=celltype, x=0, xend=Gene, colour = celltype, label=num.int(Gene) %&% " (" %&% num.round(p.Gene) %&% "%)")

.plt <- function(.df, .aes) {
    .gg.plot(.df, .aes) +
    theme(axis.ticks.y = element_blank()) +
    theme(axis.title.y = element_blank()) +
    xlab("number of cells") +
    geom_segment(size=10) +
    ggrepel::geom_text_repel(size=2.5, colour = "gray20") +
    scale_x_continuous(labels=function(x) num.int(abs(x))) +
    scale_colour_brewer(palette = "Paired", guide = FALSE)
}

plt <- ((.plt(.cell.count, .aes.cell) + scale_y_discrete(position="right")) |
        .plt(.gene.count, .aes.gene))

.file <- fig.dir %&% "/Fig_Num_Discovery.pdf"
.gg.save(filename = .file, plot = plt, width=4, height=3)
print(plt)

PDF

celltype gene N Chr TSS TES FC SD Z P
Astro GFAP 1,609 17 44,903 44,917 1.38 0.03 12.86 7.7e-38
Astro CHI3L1 687 1 203,178 203,187 1.50 0.06 10.85 2.0e-27
Astro MT2A 2,899 16 56,608 56,610 1.19 0.02 9.27 1.9e-20
Astro PRKG1 2,617 10 50,990 52,299 1.19 0.02 9.14 6.2e-20
Astro HSP90AA1 1,342 14 102,080 102,140 1.27 0.03 8.92 4.6e-19
Astro DPP10 3,236 2 114,442 115,846 1.16 0.02 8.64 5.8e-18
Astro NEBL 3,438 10 20,779 21,294 1.15 0.02 8.44 3.2e-17
Astro CPE 3,396 4 165,361 165,499 1.15 0.02 8.32 9.0e-17
Astro AQP1 208 7 30,911 30,926 1.66 0.11 7.66 1.8e-14
Astro ADCY2 2,802 5 7,396 7,831 1.15 0.02 7.58 3.5e-14
Astro TRPM3 2,971 9 70,529 71,447 1.15 0.02 7.53 4.9e-14
Astro CLU 4,400 8 27,596 27,615 1.11 0.02 7.10 1.2e-12
Astro SNED1 303 2 240,998 241,096 1.48 0.08 7.01 2.5e-12
Astro RGS6 286 14 71,932 72,567 1.48 0.08 6.79 1.1e-11
Astro GJA1 2,458 6 121,435 121,450 1.14 0.02 6.71 1.9e-11
Astro DST 3,121 6 56,457 56,955 1.13 0.02 6.68 2.4e-11
Astro HSPB1 650 7 76,302 76,305 1.28 0.05 6.46 1.0e-10
Astro RPS25 363 11 119,015 119,019 1.38 0.07 6.36 2.1e-10
Astro ITGB4 499 17 75,721 75,758 1.32 0.06 6.33 2.5e-10
Astro FYN 1,939 6 111,660 111,874 1.15 0.03 6.24 4.3e-10
Astro RASGEF1B 1,827 4 81,426 82,045 1.15 0.03 6.09 1.1e-09
Astro CD44 184 11 35,138 35,233 1.52 0.11 5.98 2.2e-09
Astro LINGO1 1,706 15 77,613 77,821 1.15 0.03 5.81 6.2e-09
Astro VCAN 242 5 83,471 83,583 1.43 0.09 5.74 9.4e-09
Astro HSPA1A 559 6 31,815 31,818 1.26 0.05 5.61 2.0e-08
Astro RFX4 1,641 12 106,583 106,763 1.15 0.03 5.60 2.2e-08
Astro PTGDS 2,893 9 136,975 136,982 1.11 0.02 5.52 3.5e-08
Astro CRYAB 500 11 111,908 111,924 1.27 0.06 5.49 4.0e-08
Astro MAPK4 1,034 18 50,560 50,732 1.18 0.04 5.44 5.2e-08
Astro PLEKHA5 562 12 19,129 19,377 1.25 0.05 5.43 5.5e-08
Astro GPM6B 2,651 X 13,770 13,939 1.11 0.02 5.42 5.9e-08
Astro FAM171A1 888 10 15,211 15,372 1.20 0.04 5.41 6.4e-08
Astro PLEC 442 8 143,915 143,977 1.28 0.06 5.31 1.1e-07
Astro CNTN1 1,818 12 40,692 41,073 1.13 0.03 5.30 1.2e-07
Astro AEBP1 318 7 44,104 44,115 1.33 0.07 5.29 1.2e-07
Astro TPT1 335 13 45,333 45,342 1.32 0.07 5.28 1.3e-07
Astro MT1E 1,330 16 56,625 56,628 1.15 0.03 5.25 1.5e-07
Astro ITPKB 1,209 1 226,631 226,740 1.16 0.03 5.22 1.8e-07
Astro FLRT2 1,319 14 85,530 85,655 1.15 0.03 5.14 2.7e-07
Astro TNC 137 9 115,019 115,119 1.51 0.12 5.14 2.8e-07
Astro SYTL4 209 X 100,674 100,733 1.40 0.09 5.08 3.7e-07
Astro RHOBTB3 1,335 5 95,713 95,825 1.15 0.03 5.06 4.2e-07
Astro PARD3 2,625 10 34,109 34,816 1.10 0.02 5.03 4.9e-07
Astro BAZ2B 1,427 2 159,318 159,617 1.14 0.03 5.01 5.5e-07
Astro SON 1,390 21 33,543 33,578 1.14 0.03 4.97 6.5e-07
Astro PDE4DIP 2,163 1 148,808 149,049 1.11 0.02 4.96 6.9e-07
Astro MAML2 2,319 11 95,976 96,344 1.11 0.02 4.92 8.6e-07
Astro LPIN1 863 2 11,677 11,828 1.18 0.04 4.90 9.5e-07
Astro TENM4 396 11 78,652 79,442 1.27 0.06 4.89 1.0e-06
Endo B2M 233 15 44,711 44,719 2.88 0.18 16.74 6.7e-63
Endo MT2A 154 16 56,608 56,610 2.56 0.20 12.35 5.1e-35
Endo TMSB10 156 2 84,905 84,907 2.46 0.19 11.86 1.9e-32
Endo CLDN5 130 22 19,523 19,528 2.59 0.21 11.57 5.6e-31
Endo IFITM3 107 11 319 328 2.39 0.21 9.76 1.6e-22
Endo HLA-B 102 6 31,269 31,358 2.34 0.21 9.33 1.1e-20
Endo BSG 87 19 571 584 2.36 0.23 8.80 1.4e-18
Endo TMSB4X 129 X 12,975 12,978 2.06 0.17 8.79 1.5e-18
Endo TSC22D1 83 13 44,432 44,578 2.34 0.23 8.52 1.5e-17
Endo FLT1 70 13 28,300 28,496 2.40 0.26 8.22 2.0e-16
Endo APOD 93 3 195,568 195,585 2.14 0.20 7.99 1.3e-15
Endo RPL13 88 16 89,560 89,565 2.05 0.20 7.37 1.8e-13
Endo RASGEF1B 83 4 81,426 82,045 2.05 0.20 7.23 4.8e-13
Endo RPS19 84 19 41,860 41,873 2.05 0.20 7.23 4.9e-13
Endo LINGO1 77 15 77,613 77,821 2.05 0.21 7.02 2.2e-12
Endo IGFBP7 72 4 57,030 57,111 2.09 0.22 6.99 2.8e-12
Endo RBMS3 76 3 28,574 30,011 2.05 0.21 6.98 2.9e-12
Endo HLA-C 67 6 31,268 31,273 2.12 0.23 6.95 3.7e-12
Endo DLC1 62 8 13,083 13,605 2.13 0.24 6.75 1.5e-11
Endo ACTB 86 7 5,526 5,564 1.91 0.19 6.62 3.6e-11
Endo PTMA 74 2 231,706 231,714 1.98 0.21 6.56 5.5e-11
Endo DUSP1 53 5 172,768 172,772 2.17 0.26 6.51 7.5e-11
Endo HLA-A 65 6 29,941 29,946 2.03 0.22 6.46 1.0e-10
Endo EEF1A1 86 6 73,515 73,524 1.88 0.18 6.45 1.1e-10
Endo RPS15 72 19 1,438 1,441 1.95 0.21 6.31 2.7e-10
Endo HSP90AA1 82 14 102,080 102,140 1.87 0.19 6.26 3.9e-10
Endo EIF1 65 17 41,688 41,693 1.96 0.22 6.14 8.3e-10
Endo RPL13A 69 19 49,487 49,493 1.92 0.21 6.08 1.2e-09
Endo EPAS1 54 2 46,293 46,387 2.04 0.24 6.06 1.4e-09
Endo RPL10 70 X 154,389 154,410 1.91 0.20 6.05 1.4e-09
Endo ST6GALNAC3 55 1 76,074 76,635 2.03 0.24 6.03 1.6e-09
Endo IFI27 61 14 94,104 94,117 1.96 0.22 5.96 2.4e-09
Endo ABCB1 59 7 87,503 87,714 1.97 0.22 5.95 2.7e-09
Endo PTPRG 66 3 61,561 62,298 1.89 0.21 5.81 6.1e-09
Endo SPARCL1 68 4 87,473 87,532 1.80 0.19 5.44 5.2e-08
Endo RPL41 53 12 56,116 56,118 1.89 0.22 5.38 7.5e-08
Endo SYNE1 50 6 152,121 152,638 1.91 0.23 5.35 9.0e-08
Endo HLA-E 53 6 30,489 30,495 1.87 0.22 5.25 1.5e-07
Endo MBNL1 53 3 152,243 152,466 1.80 0.21 4.93 8.1e-07
Ex MTRNR2L1 3,241 17 22,523 22,525 1.93 0.03 37.50 7.7e-308
Ex DHFR 17,260 5 80,626 80,655 1.18 0.01 21.58 2.7e-103
Ex MTRNR2L8 2,829 11 10,507 10,510 1.41 0.03 18.51 1.6e-76
Ex ARL17B 4,977 17 46,274 46,362 0.79 0.01 -16.56 1.4e-61
Ex ELK1 5,885 X 47,635 47,651 1.21 0.02 14.63 1.7e-48
Ex EIF5A 2,775 17 7,306 7,313 0.77 0.01 -14.09 4.5e-45
Ex NIPAL2 8,842 8 98,189 98,295 0.86 0.01 -14.02 1.2e-44
Ex MTRNR2L12 4,632 3 96,617 96,619 1.21 0.02 12.92 3.4e-38
Ex SPDYE2 3,447 7 102,551 102,563 0.80 0.01 -12.91 4.2e-38
Ex SNCG 4,460 10 86,958 86,964 0.83 0.01 -12.22 2.3e-34
Ex HSPA8 9,252 11 123,057 123,064 1.12 0.01 11.31 1.1e-29
Ex NNAT 4,573 20 37,521 37,524 1.17 0.02 10.83 2.5e-27
Ex P2RY14 5,427 3 151,212 151,279 1.16 0.02 10.73 7.7e-27
Ex LRPAP1 5,929 4 3,503 3,533 1.14 0.01 10.16 2.9e-24
Ex SSTR2 4,969 17 73,165 73,177 1.15 0.02 9.88 4.9e-23
Ex STAT4 5,406 2 191,029 191,152 0.88 0.01 -9.83 8.6e-23
Ex CNTN6 2,329 3 1,092 1,405 0.82 0.02 -9.81 1.1e-22
Ex RPH3A 7,851 12 112,570 112,899 0.90 0.01 -9.61 7.1e-22
Ex ITPR2 6,605 12 26,335 26,834 0.89 0.01 -9.59 8.4e-22
Ex PDE5A 7,744 4 119,494 119,629 0.90 0.01 -9.55 1.3e-21
Ex SEMA3A 6,486 7 83,955 84,493 0.89 0.01 -9.19 3.8e-20
Ex OR2L13 5,594 1 248,095 248,102 1.13 0.02 9.15 5.5e-20
Ex PTPN14 2,555 1 214,348 214,553 0.84 0.02 -8.96 3.2e-19
Ex EEF1A2 3,296 20 63,488 63,500 1.16 0.02 8.78 1.6e-18
Ex LRRC37A3 6,259 17 64,854 64,920 0.90 0.01 -8.36 6.1e-17
Ex PTCHD4 2,993 6 47,878 48,069 0.86 0.02 -8.24 1.7e-16
Ex PCED1B 1,681 12 47,079 47,237 0.83 0.02 -7.83 5.1e-15
Ex EGR1 1,331 5 138,465 138,470 0.81 0.02 -7.73 1.1e-14
Ex VGF 2,487 7 101,162 101,166 0.86 0.02 -7.71 1.3e-14
Ex VSTM2L 4,594 20 37,903 37,946 0.89 0.01 -7.61 2.7e-14
Ex CD81 4,281 11 2,376 2,398 1.12 0.02 7.47 8.0e-14
Ex NBPF11 1,872 1 148,102 148,153 0.84 0.02 -7.36 1.9e-13
Ex TEF 4,331 22 41,367 41,400 1.12 0.02 7.28 3.3e-13
Ex LRRC37A2 1,636 17 46,511 46,556 0.84 0.02 -7.08 1.4e-12
Ex TSC22D3 3,996 X 107,713 107,778 1.12 0.02 7.08 1.4e-12
Ex ADAMTS2 1,893 5 179,110 179,346 1.18 0.03 7.05 1.8e-12
Ex CNGB1 3,271 16 57,882 57,972 1.13 0.02 6.98 3.0e-12
Ex MSH4 1,641 1 75,796 75,914 1.19 0.03 6.95 3.6e-12
Ex PLA2G4A 732 1 186,828 186,989 0.78 0.03 -6.94 3.8e-12
Ex SLC38A2 4,492 12 46,358 46,373 1.11 0.02 6.89 5.4e-12
Ex FOS 89 14 75,278 75,283 0.51 0.05 -6.89 5.6e-12
Ex ST18 368 8 52,110 52,461 0.70 0.04 -6.88 6.2e-12
Ex OTOGL 1,513 12 80,099 80,381 0.84 0.02 -6.83 8.8e-12
Ex RASD2 1,808 22 35,540 35,554 1.17 0.03 6.55 5.7e-11
Ex DNAJB1 3,696 19 14,514 14,530 1.11 0.02 6.46 1.1e-10
Ex HRH3 1,074 20 62,214 62,221 1.21 0.04 6.36 2.0e-10
Ex GPS2 1,668 17 7,311 7,316 1.17 0.03 6.30 2.9e-10
Ex DUSP26 3,574 8 33,591 33,601 1.11 0.02 6.27 3.7e-10
Ex DUSP6 696 12 89,347 89,353 0.79 0.03 -6.17 6.7e-10
Ex PMPCB 3,522 7 103,297 103,330 1.11 0.02 6.12 9.3e-10
Ex NPTX2 1,317 7 98,617 98,630 0.85 0.02 -6.10 1.1e-09
Ex KCNV2 875 9 2,717 2,731 1.23 0.04 6.07 1.3e-09
Ex HLA-DRB1 184 6 32,578 32,590 0.66 0.05 -5.96 2.5e-09
Ex MSMO1 3,701 4 165,327 165,344 1.10 0.02 5.90 3.6e-09
Ex PHLDB2 466 3 111,732 111,977 0.77 0.03 -5.82 5.7e-09
Ex PCSK6 1,238 15 101,297 101,526 0.85 0.02 -5.81 6.1e-09
Ex PILRB 2,763 7 100,352 100,368 0.90 0.02 -5.79 7.2e-09
Ex RPRM 1,069 2 153,477 153,479 1.19 0.04 5.75 9.1e-09
Ex MYOM2 2,043 8 2,045 2,166 0.88 0.02 -5.73 1.0e-08
Ex YIPF6 2,913 X 68,498 68,538 1.11 0.02 5.71 1.1e-08
Ex LAMP5 3,370 20 9,514 9,531 1.10 0.02 5.71 1.1e-08
Ex TSPAN17 1,337 5 176,647 176,660 1.17 0.03 5.70 1.2e-08
Ex IL34 1,963 16 70,579 70,661 1.14 0.03 5.68 1.4e-08
Ex SYT6 340 1 114,089 114,154 1.35 0.07 5.67 1.4e-08
Ex TMEM233 1,512 12 119,593 119,644 1.16 0.03 5.64 1.7e-08
Ex IL1RAP 1,726 3 190,514 190,660 0.87 0.02 -5.59 2.2e-08
Ex CTXN3 895 5 127,649 127,659 0.83 0.03 -5.56 2.6e-08
Ex TIMP2 3,291 17 78,852 78,926 1.10 0.02 5.54 3.0e-08
Ex ZDHHC2 2,055 8 17,156 17,225 0.89 0.02 -5.52 3.4e-08
Ex CD24 583 6 106,969 106,976 1.25 0.05 5.52 3.4e-08
Ex DUSP8 2,317 11 1,554 1,573 1.12 0.02 5.50 3.8e-08
Ex IGFBP5 3,198 2 216,672 216,696 1.10 0.02 5.47 4.6e-08
Ex SMAD4 2,183 18 51,028 51,086 1.12 0.02 5.43 5.5e-08
Ex NR4A1 788 12 52,022 52,060 0.83 0.03 -5.39 7.2e-08
Ex CD53 882 1 110,871 110,900 0.84 0.03 -5.37 7.8e-08
Ex TMED2 2,832 12 123,584 123,599 1.11 0.02 5.36 8.5e-08
Ex TCF7L2 2,481 10 112,950 113,168 0.90 0.02 -5.34 9.2e-08
Ex HUNK 1,723 21 31,873 32,045 0.88 0.02 -5.29 1.2e-07
Ex TMEM144 785 4 158,201 158,256 0.83 0.03 -5.28 1.3e-07
Ex PPAT 1,939 4 56,393 56,436 1.13 0.03 5.26 1.5e-07
Ex CFAP61 1,540 20 20,052 20,361 0.88 0.02 -5.24 1.6e-07
Ex GRIK1 1,903 21 29,536 29,941 0.89 0.02 -5.22 1.8e-07
Ex TTF2 1,118 1 117,060 117,108 0.86 0.03 -5.21 1.9e-07
Ex GRIK3 2,112 1 36,795 37,035 1.12 0.02 5.19 2.1e-07
Ex SPTSSB 1,285 3 161,344 161,373 0.87 0.02 -5.14 2.8e-07
Ex MTRNR2L6 198 7 142,666 142,668 1.42 0.10 5.12 3.0e-07
Ex COBLL1 1,434 2 164,653 164,844 0.88 0.02 -5.06 4.2e-07
Ex PLD1 931 3 171,600 171,811 0.85 0.03 -5.06 4.2e-07
Ex HTR4 2,094 5 148,451 148,678 0.90 0.02 -5.06 4.3e-07
Ex MKNK2 1,395 19 2,037 2,052 1.14 0.03 5.04 4.7e-07
Ex ZDHHC22 2,654 14 77,131 77,143 1.10 0.02 4.98 6.4e-07
Ex MYO1E 773 15 59,132 59,373 0.84 0.03 -4.96 6.9e-07
Ex CBFB 1,309 16 67,028 67,102 1.15 0.03 4.95 7.3e-07
Ex COMMD2 2,506 3 149,738 149,753 1.10 0.02 4.94 7.9e-07
Ex NEDD9 923 6 11,183 11,383 0.85 0.03 -4.92 8.9e-07
Ex CREG1 2,367 1 167,529 167,554 1.11 0.02 4.90 9.7e-07
In MTRNR2L1 732 17 22,523 22,525 1.83 0.07 16.59 7.7e-62
In DHFR 4,437 5 80,626 80,655 1.20 0.02 12.16 4.9e-34
In RASGEF1B 6,371 4 81,426 82,045 1.13 0.01 9.71 2.8e-22
In MTRNR2L8 761 11 10,507 10,510 1.40 0.05 9.36 8.1e-21
In SLC26A3 3,477 7 107,765 107,804 1.17 0.02 9.32 1.1e-20
In IQCJ-SCHIP1 4,990 3 158,962 159,898 1.12 0.02 8.21 2.1e-16
In PCSK1N 5,724 X 48,831 48,836 1.11 0.01 7.89 3.1e-15
In LINGO1 5,398 15 77,613 77,821 1.10 0.02 7.33 2.3e-13
In ARL17B 805 17 46,274 46,362 0.78 0.03 -7.25 4.0e-13
In CLU 2,787 8 27,596 27,615 1.15 0.02 7.17 7.3e-13
In MTRNR2L12 1,121 3 96,617 96,619 1.22 0.04 6.62 3.7e-11
In EIF5A 369 17 7,306 7,313 0.72 0.04 -6.54 6.2e-11
In FTL 4,234 19 48,965 48,967 1.10 0.02 6.22 5.0e-10
In CENPP 626 9 92,325 92,621 0.80 0.03 -5.75 8.8e-09
In SNCG 1,700 10 86,958 86,964 0.88 0.02 -5.23 1.7e-07
Microglia SPP1 1,391 4 87,975 87,984 1.58 0.04 17.12 1.0e-65
Microglia PLXDC2 2,931 10 19,816 20,290 1.28 0.02 13.26 3.8e-40
Microglia SLC1A3 1,513 5 36,606 36,689 1.37 0.04 12.38 3.4e-35
Microglia RASGEF1B 439 4 81,426 82,045 1.59 0.07 9.92 3.5e-23
Microglia APOE 842 19 44,905 44,910 1.39 0.05 9.67 4.0e-22
Microglia MBNL1 737 3 152,243 152,466 1.39 0.05 9.11 8.0e-20
Microglia DOCK8 1,064 9 214 466 1.31 0.04 8.80 1.4e-18
Microglia HS3ST4 1,026 16 25,691 26,138 1.31 0.04 8.65 5.1e-18
Microglia FKBP5 701 6 35,573 35,729 1.38 0.05 8.64 5.5e-18
Microglia FRMD4A 2,031 10 13,643 14,463 1.21 0.03 8.63 5.9e-18
Microglia RPS19 519 19 41,860 41,873 1.44 0.06 8.51 1.7e-17
Microglia CACNA1A 302 19 13,206 13,634 1.60 0.09 8.45 3.0e-17
Microglia ARHGAP24 1,230 4 85,475 86,003 1.26 0.04 8.30 1.1e-16
Microglia FTL 986 19 48,965 48,967 1.29 0.04 7.97 1.5e-15
Microglia DOCK10 545 2 224,765 225,043 1.39 0.06 7.89 3.0e-15
Microglia MEF2C 1,291 5 88,717 88,905 1.24 0.03 7.74 9.9e-15
Microglia HDAC9 812 7 18,086 19,003 1.31 0.05 7.73 1.0e-14
Microglia UBE2E2 660 3 23,203 23,592 1.34 0.05 7.55 4.5e-14
Microglia FOXN3 1,091 14 89,124 89,620 1.25 0.04 7.52 5.5e-14
Microglia FMN1 439 15 32,765 33,195 1.42 0.07 7.51 5.8e-14
Microglia ELMO1 974 7 36,854 37,450 1.27 0.04 7.49 7.0e-14
Microglia LHFPL2 507 5 78,485 78,771 1.38 0.06 7.31 2.7e-13
Microglia CSGALNACT1 558 8 19,404 19,759 1.35 0.06 7.24 4.3e-13
Microglia DOCK4 1,808 7 111,726 112,207 1.18 0.03 7.24 4.4e-13
Microglia GRID2 588 4 92,303 93,811 1.34 0.05 7.22 5.1e-13
Microglia RPL13 391 16 89,560 89,565 1.42 0.07 7.16 8.3e-13
Microglia LINGO1 383 15 77,613 77,821 1.43 0.07 7.15 8.8e-13
Microglia MAML2 977 11 95,976 96,344 1.25 0.04 7.02 2.2e-12
Microglia PICALM 635 11 85,957 86,070 1.31 0.05 6.96 3.4e-12
Microglia BHLHE41 310 12 26,120 26,126 1.47 0.08 6.96 3.5e-12
Microglia VSIG4 303 X 66,021 66,041 1.47 0.08 6.92 4.4e-12
Microglia FGD4 512 12 32,399 32,647 1.35 0.06 6.91 4.9e-12
Microglia TBC1D5 504 3 17,157 18,445 1.35 0.06 6.90 5.3e-12
Microglia CSF1R 423 5 150,053 150,114 1.38 0.07 6.82 8.9e-12
Microglia MEF2A 1,099 15 99,565 99,717 1.22 0.04 6.63 3.3e-11
Microglia LAPTM5 293 1 30,732 30,758 1.46 0.08 6.62 3.6e-11
Microglia CELF2 887 10 10,798 11,337 1.25 0.04 6.62 3.7e-11
Microglia SNCA 216 4 89,700 89,839 1.54 0.10 6.60 4.0e-11
Microglia TMSB4X 855 X 12,975 12,978 1.25 0.04 6.55 5.8e-11
Microglia SLC9A9 763 3 143,265 143,849 1.26 0.05 6.49 8.4e-11
Microglia GLDN 306 15 51,341 51,409 1.43 0.08 6.43 1.2e-10
Microglia SLC26A3 199 7 107,765 107,804 1.55 0.11 6.43 1.3e-10
Microglia RUNX1 635 21 34,787 36,005 1.28 0.05 6.40 1.5e-10
Microglia GNAQ 670 9 77,716 78,032 1.28 0.05 6.38 1.7e-10
Microglia OXR1 696 8 106,359 106,753 1.27 0.05 6.36 2.0e-10
Microglia CYFIP1 372 15 22,867 22,982 1.38 0.07 6.30 3.0e-10
Microglia ANKRD44 748 2 196,967 197,312 1.25 0.05 6.21 5.4e-10
Microglia PSAP 390 10 71,816 71,852 1.36 0.07 6.17 7.0e-10
Microglia SSH2 883 17 29,625 29,931 1.23 0.04 6.12 9.3e-10
Microglia NHSL1 204 6 138,422 138,693 1.51 0.10 6.11 9.7e-10
Microglia RPLP1 286 15 69,452 69,457 1.42 0.08 6.09 1.1e-09
Microglia SH3RF3 515 2 109,129 109,505 1.30 0.06 6.08 1.2e-09
Microglia APOC1 125 19 44,914 44,920 1.66 0.14 6.08 1.2e-09
Microglia TBXAS1 664 7 139,777 140,021 1.26 0.05 6.07 1.3e-09
Microglia LDLRAD4 867 18 13,217 13,653 1.23 0.04 6.07 1.3e-09
Microglia APBB1IP 997 10 26,438 26,568 1.21 0.04 5.96 2.5e-09
Microglia ZFHX3 511 16 72,782 73,892 1.29 0.06 5.94 2.8e-09
Microglia ADGRG1 235 16 57,610 57,666 1.45 0.09 5.94 2.8e-09
Microglia SKAP2 363 7 26,667 26,996 1.35 0.07 5.92 3.2e-09
Microglia DPYD 322 1 97,077 97,995 1.38 0.07 5.92 3.2e-09
Microglia SLTM 163 15 58,879 58,934 1.55 0.12 5.90 3.6e-09
Microglia MAN2A1 151 5 109,689 109,870 1.56 0.12 5.81 6.4e-09
Microglia JAZF1 411 7 27,830 28,181 1.32 0.06 5.79 7.0e-09
Microglia ALCAM 166 3 105,366 105,577 1.53 0.11 5.79 7.2e-09
Microglia CD81 192 11 2,376 2,398 1.48 0.10 5.71 1.1e-08
Microglia RPL28 222 19 55,385 55,404 1.44 0.09 5.69 1.2e-08
Microglia ST6GAL1 1,078 3 186,930 187,079 1.19 0.04 5.64 1.7e-08
Microglia EPB41L2 797 6 130,839 131,064 1.22 0.04 5.62 2.0e-08
Microglia STARD13 157 13 33,103 33,351 1.53 0.12 5.60 2.1e-08
Microglia ITPR2 1,031 12 26,335 26,834 1.19 0.04 5.57 2.6e-08
Microglia ZFP36L1 406 14 68,787 68,797 1.31 0.06 5.56 2.6e-08
Microglia RPL13A 255 19 49,487 49,493 1.40 0.08 5.54 3.0e-08
Microglia TANC2 247 17 63,009 63,428 1.40 0.09 5.51 3.5e-08
Microglia C1QC 320 1 22,643 22,649 1.35 0.07 5.51 3.6e-08
Microglia ZEB2 620 2 144,364 144,522 1.24 0.05 5.51 3.7e-08
Microglia TAB2 378 6 149,218 149,412 1.32 0.07 5.50 3.7e-08
Microglia ACTB 334 7 5,526 5,564 1.34 0.07 5.47 4.4e-08
Microglia HSP90AA1 420 14 102,080 102,140 1.30 0.06 5.46 4.9e-08
Microglia PNISR 346 6 99,398 99,426 1.33 0.07 5.43 5.7e-08
Microglia AUTS2 319 7 69,598 70,794 1.34 0.07 5.41 6.5e-08
Microglia EIF4G3 414 1 20,806 21,177 1.30 0.06 5.39 7.2e-08
Microglia SRGAP2 956 1 206,203 206,465 1.19 0.04 5.38 7.4e-08
Microglia HLA-B 231 6 31,269 31,358 1.40 0.09 5.34 9.1e-08
Microglia IFNGR1 254 6 137,197 137,220 1.37 0.08 5.24 1.6e-07
Microglia LPCAT2 389 16 55,509 55,587 1.30 0.06 5.23 1.7e-07
Microglia P2RY12 357 3 151,336 151,385 1.31 0.07 5.21 1.9e-07
Microglia RPS27A 203 2 55,231 55,236 1.42 0.10 5.19 2.1e-07
Microglia N4BP2L2 382 13 32,432 32,539 1.29 0.06 5.16 2.4e-07
Microglia FOXP1 349 3 70,952 71,584 1.31 0.07 5.16 2.4e-07
Microglia SPIDR 287 8 47,260 47,737 1.34 0.08 5.16 2.4e-07
Microglia SHTN1 290 10 116,881 117,127 1.34 0.08 5.13 2.9e-07
Microglia CD74 709 5 150,400 150,413 1.21 0.04 5.09 3.5e-07
Microglia BNC2 311 9 16,409 16,871 1.32 0.07 5.09 3.5e-07
Microglia TCF12 579 15 56,918 57,300 1.23 0.05 5.09 3.6e-07
Microglia RAP1A 295 1 111,542 111,717 1.33 0.08 5.07 3.9e-07
Microglia DTNA 193 18 34,493 34,892 1.42 0.10 5.07 3.9e-07
Microglia MED13L 553 12 115,957 116,278 1.24 0.05 5.07 4.0e-07
Microglia SLC8A1 799 2 40,097 40,612 1.19 0.04 5.06 4.2e-07
Microglia MYO1E 155 15 59,132 59,373 1.47 0.11 5.05 4.4e-07
Microglia SORL1 688 11 121,452 121,634 1.21 0.05 5.03 4.8e-07
Microglia ARHGAP15 658 2 143,091 143,769 1.21 0.05 5.03 5.0e-07
Microglia ANKRD12 208 18 9,136 9,286 1.40 0.09 5.01 5.4e-07
Microglia TMSB10 236 2 84,905 84,907 1.37 0.09 5.01 5.5e-07
Microglia MAML3 639 4 139,716 140,155 1.22 0.05 5.01 5.6e-07
Microglia ATM 333 11 108,222 108,370 1.31 0.07 5.00 5.7e-07
Microglia SAMD12 137 8 118,189 118,623 1.49 0.12 4.97 6.8e-07
Microglia SIK3 246 11 116,843 117,099 1.36 0.08 4.96 7.0e-07
Microglia ZFP36L2 307 2 43,222 43,227 1.32 0.07 4.96 7.2e-07
Microglia WWOX 404 16 78,099 79,213 1.27 0.06 4.94 7.9e-07
Microglia TNRC6B 343 22 40,044 40,336 1.30 0.07 4.94 7.9e-07
Microglia DIAPH2 595 X 96,684 97,605 1.22 0.05 4.93 8.0e-07
Microglia FHIT 567 3 59,747 61,252 1.23 0.05 4.93 8.2e-07
Microglia C3 500 19 6,677 6,731 1.24 0.05 4.92 8.7e-07
Microglia ARMC9 100 2 231,198 231,377 1.57 0.14 4.89 1.0e-06
Microglia GPATCH2L 150 14 76,151 76,255 1.46 0.11 4.89 1.0e-06
Oligo NTM 7,395 11 131,370 132,337 1.15 0.01 11.70 1.2e-31
Oligo ARL17B 382 17 46,274 46,362 0.60 0.03 -10.22 1.6e-24
Oligo HSPA1B 461 6 31,827 31,831 0.63 0.03 -10.06 8.2e-24
Oligo TLE4 3,345 9 79,571 79,727 1.14 0.02 7.82 5.5e-15
Oligo PRKX 573 X 3,604 3,714 1.36 0.06 7.39 1.5e-13
Oligo CHRM5 625 15 33,968 34,068 0.75 0.03 -7.16 8.1e-13
Oligo ABCA6 651 17 69,078 69,142 1.30 0.05 6.76 1.4e-11
Oligo PTPRM 831 18 7,566 8,407 1.26 0.04 6.66 2.7e-11
Oligo ADGRL3 2,857 4 61,201 62,079 0.88 0.02 -6.59 4.4e-11
Oligo ZNF365 2,198 10 62,374 62,481 1.15 0.02 6.50 8.1e-11
Oligo KCNIP4 1,472 4 20,728 21,949 1.18 0.03 6.38 1.8e-10
Oligo ADAMTS18 618 16 77,247 77,436 1.27 0.05 6.11 9.9e-10
Oligo HSD11B1 375 1 209,686 209,735 0.74 0.04 -5.93 3.0e-09
Oligo LURAP1L 457 9 12,775 12,824 1.31 0.06 5.89 3.9e-09
Oligo FKBP5 1,885 6 35,573 35,729 1.14 0.03 5.88 4.0e-09
Oligo RHOBTB1 1,289 10 60,869 61,002 1.17 0.03 5.65 1.6e-08
Oligo CPQ 2,658 8 96,645 97,150 1.11 0.02 5.61 2.0e-08
Oligo LAMA2 2,688 6 128,883 129,517 1.11 0.02 5.52 3.4e-08
Oligo PDK4 1,037 7 95,583 95,597 1.18 0.04 5.46 4.9e-08
Oligo CARNS1 1,529 11 67,414 67,426 0.87 0.02 -5.30 1.2e-07
Oligo LIFR 1,840 5 38,474 38,609 1.13 0.03 5.26 1.4e-07
Oligo HSPA1A 2,228 6 31,815 31,818 0.90 0.02 -5.23 1.7e-07
Oligo MDGA2 1,052 14 46,839 47,676 1.17 0.04 5.07 4.0e-07
Oligo MT3 1,287 16 56,589 56,592 1.15 0.03 5.04 4.6e-07
Oligo NKX6-2 1,677 10 132,783 132,787 1.13 0.03 5.04 4.6e-07
Oligo PTPN13 1,448 4 86,594 86,816 1.14 0.03 5.01 5.3e-07
Oligo DCC 855 18 52,340 53,536 1.18 0.04 4.99 6.1e-07
Oligo SORCS2 851 4 7,192 7,743 1.18 0.04 4.99 6.1e-07
Oligo TTC3 1,825 21 37,073 37,204 1.12 0.03 4.90 9.7e-07
OPC DSCAM 8,292 21 40,010 40,848 1.12 0.01 10.30 6.8e-25
OPC LRRC4C 7,942 11 40,114 41,460 1.10 0.01 8.86 8.2e-19
OPC LRP1B 7,092 2 140,231 142,132 1.10 0.01 8.17 3.0e-16
OPC NOVA1 3,192 14 26,443 26,599 1.15 0.02 7.95 1.9e-15
OPC NLGN1 5,979 3 173,396 174,287 1.11 0.01 7.75 9.1e-15
OPC PTPRZ1 4,661 7 121,873 122,063 1.12 0.02 7.70 1.3e-14
OPC LINGO1 1,267 15 77,613 77,821 1.22 0.03 7.08 1.4e-12
OPC VCAN 3,832 5 83,471 83,583 1.12 0.02 7.06 1.6e-12
OPC NTM 5,256 11 131,370 132,337 1.10 0.02 6.97 3.1e-12
OPC NRG3 2,130 10 81,875 82,988 1.16 0.02 6.80 1.1e-11
OPC RASGEF1B 1,293 4 81,426 82,045 1.20 0.03 6.74 1.6e-11
OPC MAML2 2,153 11 95,976 96,344 1.15 0.02 6.41 1.5e-10
OPC FGF12 2,740 3 192,139 192,768 1.13 0.02 6.31 2.7e-10
OPC OLIG1 1,459 21 33,070 33,073 1.18 0.03 6.23 4.6e-10
OPC KAZN 2,738 1 13,892 15,119 1.12 0.02 5.89 3.8e-09
OPC SLC26A3 550 7 107,765 107,804 1.26 0.05 5.54 3.1e-08
OPC ATRNL1 2,230 10 115,093 115,949 1.12 0.02 5.32 1.0e-07
OPC FHIT 2,169 3 59,747 61,252 1.12 0.02 5.23 1.7e-07
OPC PPP2R2B 1,949 5 146,581 147,085 1.12 0.03 5.10 3.4e-07
OPC NCAM1 2,059 11 112,961 113,279 1.12 0.02 5.01 5.4e-07
OPC ZBTB20 2,699 3 114,314 115,148 1.10 0.02 5.01 5.5e-07
Per HSP90AA1 164 14 102,080 102,140 4.66 0.35 20.75 1.1e-95